A depth model of an object, or a depth profile of the object, may be obtained according to a number of range imaging techniques. Many processes for obtaining depth models or profiles of objects, or ranges to such objects, operate by projecting visible or invisible light from a projector or other source, receiving reflections of the projected light by a sensor, and interpreting such reflections by one or more computer processors. For example, a structured-light three-dimensional scanner may be used to illuminate a scene with a specially designed light pattern, e.g., horizontal and vertical lines, points or patterns, and depths to objects within the scene may be determined using images of the reflected light. As another example, a time-of-flight sensor may also be used to illuminate a scene with points of light, to collect reflections of the light from aspects of the scene. Times elapsed between an emission of light and a return of the light to each pixel may be measured and multiplied by the speed of light to determine distances to aspects corresponding to each pixel.
Imaging systems for obtaining depth models or profiles of objects, or ranges to such objects, typically project light onto a scene in regular, evenly distributed patterns, e.g., a raster pattern, or a pattern by which an area is scanned in straight lines from side-to-side and from top-to-bottom, or randomized patterns. Likewise, reflections of projected light are typically captured and reflected in the order or manner in which such reflections are received. Thus, imaging systems for obtaining depth models or profiles of scenes, or ranges to objects within such scenes, typically operate without regard to the content of the scenes, treating each of the aspects within such scenes as being equally relevant or having identical levels of complexity. Where imaging systems are used to obtain depth models, depth profiles or ranges from a scene that features aspects having both low degrees of entropy or depth variation, and high degrees of entropy or depth variation, end products generated by such systems represent the scene in a homogenous manner. Frequently, points or patterns of light that are projected onto and reflected from surfaces with stable, consistent or infrequently changing depths or ranges are wasted, as such surfaces may be accurately described with a comparatively low number of depths or ranges. Conversely, surfaces featuring unstable, inconsistent or frequently changing depths or ranges are not accurately described by a set of points that are generally cast upon such surfaces according to a raster pattern, or are reflected from such surfaces and interpreted according to a raster.
Currently, the only known technique for enhancing the accuracy and precision of depth models or depth profiles of objects, or ranges to such objects, is to increase the density of the points of light projected upon such objects. Increasing the density of points of light projected upon a surface, however, would require larger and substantially more complicated projector equipment, and capturing and interpreting reflections of such light would consume increased amounts of available bandwidth, processing power, storage capacity or other computer-based resources. Moreover, increasing the density of points of light projected upon a surface may have limited effectiveness in sampling an object's shape at its sharpest points, e.g., corners or edges of the object, and would only increase the amount of resources being wasted when sampling surfaces of objects that are stable, consistent or infrequently changing.